Healthcare System Dynamics

Similar documents
Big Data Phenomics in the VA. Outline

Extracting Physician Group Intelligence from Electronic Health Records to Support Evidence Based Medicine

John D Amore Clinfometrics, Inc. 59 Washington St. Wellesley, MA December 1, To the Health Design Challenge:

Introduction to the Partners Biobank Portal. December 2016

SAGE. Nick Beard Vice President, IDX Systems Corp.

Application of AI in Healthcare. Alistair Erskine MD MBA Chief Informatics Officer

Electronic Support for Public Health Vaccine Adverse Event Reporting System (ESP:VAERS)

Quality Improvement through HIT

Data Structures vs. Study Results:

Improving Obesity Prevention in a Health Maintenance Organization

Ohio Department of Health Seasonal Influenza Activity Summary MMWR Week 14 April 1-7, 2012

LibreHealth EHR Student Exercises

Uses of the NIH Collaboratory Distributed Research Network

LifelogExplorer: A Tool for Visual Exploration of Ambulatory Skin Conductance Measurements in Context

The i2b2 transmart Foundation Community Meeting

Can Pediatricians Provide Case-Management and Long-Term Follow-Up Data for their Patients with PCH?

402 Intro to Clinical Thinking, Winter 2010 Final Group Presentation, Group 1

Current State of Visualization of EHR data: What's needed? What's next? (Session No: S50)

William S. Bush, PhD, MS Assistant Professor Department of Epidemiology and Biostatistics Institute for Computational Biology

EMR Big Data and Clinical Research. -Distributed Research Network and Common Data Model-

Factor 3: The practice measures or receives data on at least three chronic or acute care clinical measures

Rationale for creating a PBRN in chiropractic. Cheryl Hawk, DC, PhD Associate Vice President of Research and Health Policy

Visualizing Temporal Patterns by Clustering Patients

Advancing Science and Challenges to Departments and to Graduate Training in Research. Linda Smith Indiana University

10/5/2015. Advances in Pediatric Neuropsychology Test Interpretation Part I: Importance of Considering Normal Variability. Financial Disclosures

Tobacco Use: Screening & Cessation Intervention

Design Issues for Electronics Health Records Using Cloud Computing Technologies

South Carolina s Diabetes Prevention Toolkit for Physicians and Health Care Teams. Gerald Wilson, MD

Note: This is an authorized excerpt from 2016 Healthcare Benchmarks: Digital Health To download the entire report, go to

Evaluation of Clinical Text Segmentation to Facilitate Cohort Retrieval

CLINICIAN-LED E-HEALTH RECORDS. (AKA GETTING THE LITTLE DATA RIGHT) Dr Heather Leslie Ocean Informatics/openEHR Foundation

Predictive and Similarity Analytics for Healthcare

How Doctors Feel About Electronic Health Records. National Physician Poll by The Harris Poll

Does Machine Learning. In a Learning Health System?

Kaiser Permanente Convergent Medical Terminology (CMT)

From EHR Immunization Requirements to a Validation and Recognition Program

QUALITY IMPROVEMENT TOOLS

ESPnet: An Overview of Electronic Case Reporting using EHRs

A Context-Sensitive Support System for Medical Diagnosis Discovery based on Symptom Matching

IBM Patient Care and Insights: Utilizing Analytics to Deliver Impactful Care Management

HPV Vaccination. Steps for Increasing. in Practice. An Action Guide to Implement Evidence-based Strategies for Clinicians*

The Power and Promise of Digital Health

Using Health IT to Support Oral Health Integration: Dealing with Common Barriers. Jeff Hummel, MD, MPH Qualis Health November 5, 2015

Delayed Initiation of Insulin Therapy and Glycemic Control in Patients Who Decline Insulin

Semantic Alignment between ICD-11 and SNOMED-CT. By Marcie Wright RHIA, CHDA, CCS

May 2016 CTC/OHIC Measure Specifications

TITLE: A Data-Driven Approach to Patient Risk Stratification for Acute Respiratory Distress Syndrome (ARDS)

Ohio Department of Health Seasonal Influenza Activity Summary MMWR Week 3 January 13-19, 2013

How to Advance Beyond Regular Data with Text Analytics

Keeping Abreast of Breast Imagers: Radiology Pathology Correlation for the Rest of Us

Meaningful Use - Core Measure 5 Record Smoking Status Configuration Guide

Body Mass Index Growth Module in the Michigan Care Improvement Registry. Frequently Asked Questions

Ohio Department of Health Seasonal Influenza Activity Summary MMWR Week 7 February 10-16, 2013

AVADENT-WAGNER EZ GUIDE PROTOCOL

Infrastructure for Clinical Data Exchange

PHYSICIAN QUALITY REPORTING SYSTEM REGISTRY

Agenda. Immunization Registry Reporting in Community Health Centers. Presented by: Ben Pierson Program Manager Health Information Exchange

An Ontology for Healthcare Quality Indicators: Challenges for Semantic Interoperability

New Group Reading Test Level 3A

Implementation of a Clinical Trial Matching System. Session #225, March 8, 2018 Tufia Haddad, M.D.

Welcome to SeeYourChart

The openehr approach. Background. Approach. Heather Leslie, July 17, 2014

ORC: an Ontology Reasoning Component for Diabetes

Behavioral insights to improve healthcare quality

TITLE: Novel Visualization of Large Health Related Data Sets - NPHRD

Final Meaningful Use Objectives for 2017

Implementing PROMIS for Routine Screening in Ambulatory Cancer Care

Smoking Cessation Interventions In Hospital Settings: Implementing the Evidence

Hans Erik Henriksen, CEO Healthcare DENMARK

Arizona Society of Pathologists

Pragmatic Sepsis Care For Providers: Aligning evidence, guidelines, mandates and policy to inform your daily practice.

Anthem Blue Cross and Blue Shield Commercial Professional Reimbursement Policy

Final Meaningful Use Objectives for Program Year 2018

Obstructive Sleep Apnea Syndrome. Common sleep disorder causes high blood pressure and heart attacks

Leveraging Data for Targeted Patient Population Health Improvements

HIMSS Corporate Membership Benefits

Stage 2 Meaningful Use: Core Objectives. James R. Christina, DPM Director Scientific Affairs APMA

User Guide Seeing and Managing Patients with AASM SleepTM

A Case Study on Visual Analytics for Optimizing Drug Duplicate Alerts in a Medication Clinical Decision Support System

Abstract. Introduction. Providers stand to lose $1.15 billion in CMS reimbursements under MU

A Prospective Comparison of Two Commercially Available Hospital Admission Risk Scores

NFSC 370L NUTRITION ASSESSMENT LABORATORY SPRING 2009 Dr. Katie Silliman Ashleigh Eastman Vanessa Welden

Integrating the Healthcare Enterprise (IHE) Structured Data Capture (SDC) Use Case: Cancer Registry Reporting

Evaluation of Real-time Outbreak and Disease Surveillance (RODS) system in Taiwan

Clinical Observation Modeling

Using the NIH Collaboratory's and PCORnet's distributed data networks for clinical trials and observational research - A preview

HEALTHCARE AI DEVELOPMENT CYCLE

Rebooting Cancer Data Through Structured Data Capture GEMMA LEE NAACCR CONFERENCE JUNE, 2017

The North American Association of Central Cancer Registries, Inc. (NAACCR) Interoperability Activities

The 100,000 Genomes Project

Meaningful Use Stage 2: ONC Request for Comments. Ivy Baer, Jennifer Faerberg

2010 Fall i2b2 Academic Users Group Meeting: Field Report from the CARRAnet Registry Informatics Development Team

Karen Sakala, RN BSN, PCMH-CCE Diabetes Advisory Council June 20, 2014

How to enter and update Immunizations using HF71

The i2b2 transmart Foundation Community Meeting

Site Training. The 39 th Multicenter Airway Research Collaboration (MARC-39) Study

ANXIETY A brief guide to the PROMIS Anxiety instruments:

Recent trends in health care legislation have led to a rise in

(happiness, input, feedback, improvement)

Transcription:

Healthcare System Dynamics http://healthcaresystemdynamics.org Denis Agniel 1, Nick Benik 1, Katy Borner 2, Nick Brown 1, Daniel Halsey 2, Isaac Kohane 1, Daniel O Donnell 2, Griffin Weber 1 1 Harvard Medical School; 2 Indiana University weber@hms.harvard.edu Big Data to Knowledge (BD2K), NIH/NCI U01 CA198934 Healthcare System Dynamics Clinical data reflect both patients health AND their interactions with the healthcare system. Patient Pathophysiology Healthcare System Dynamics Data Quality Patient Demographics Diagnoses Laboratory Test Results Vital Signs Genetic Markers Number of Observations Time of Day of Observations Time Between Observations Cost of a Test or Treatment Clinical Setting / Clinician Type Data Entry Errors Dictation Mistakes Data Compression Loss Unstructured Data Missing Data Patient Clinical Encounter Clinician Healthcare System Dynamics Patient Pathophysiology Normal Abnormal EHR Normal Best Outcomes Moderate Outcomes Electronic Health Record Data Abnormal Moderate Outcomes Worst Outcomes 1

Daily HSD Cycles in Clinical Data Weekly HSD Cycles in Clinical Data 2

Yearly HSD Cycles in Clinical Data HSD Impact on Time Intervals between Visits 3

Using HSD To Predict Outcomes Survival 3 Years After a WBC Test 4

Fraction of Patients with WBC by Value and Hour 3-Year Survival After a WBC by Value and Hour 5

Fraction of Patients with WBC by Value and Weekday 3-Year Survival After a WBC by Value and Weekday 6

Fraction of Patients with WBC by Value and Interval 3-Year Survival After a WBC by Value and Interval 7

Predicting Survival from Ordering a Lab Test Higher Survival Rate Higher Mortality Rate Predicting Survival Using Lab Value & HSD Value More Predictive HSD More Predictive 8

30 Day Readmission Rate After a WBC Test Using HSD To Derive Normal Ranges 9

Deriving Normal Ranges of Lab Test Values Weber GM, Kohane IS. Extracting physician group intelligence from electronic health records to support evidence based medicine. PLoS One. 2013 May 29;8(5):e64933. Deriving Normal Ranges by Age Group 1000 Pediatric WBC Repeat Intervals Days to Repeat 100 10 1 M0 M1 5 M6 23 Y2 5 Y6 10 0.1 2 4 6 8 10 12 14 16 18 20 22 WBC 10

Three Types of Laboratory Tests 1 Lab Categories 0.9 Normalized Time to Repeat 0.8 0.7 0.6 0.5 0.4 0.3 0.2 WBC HDL Hgb A1c 0.1 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Normalized Value Three Types of Laboratory Tests 1 Lab Categories 0.9 Normalized Time to Repeat 0.8 0.7 0.6 0.5 0.4 0.3 0.2??? 0.1 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Normalized Value 11

Three Types of Laboratory Tests 1 Lab Categories Normalized Time to Repeat 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 hcg (Human ChorionicGonadotropin) 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Normalized Value??? Predicting Survival Using Fact Count 12

Fact Count (over 8.5 years) by Age & Gender Fact Count Growth & Survival Chart (Male) Horizontal blue curves with rectangular labels are five year fact count percentiles. Vertical red curves with hexagonal labels are three year survival curves. Data are from BWH and MGH from 7/28/2001 to 7/27/2009. All patients had at least one fact between 7/28/2005 and 7/27/2006. The current age is the patient age on 7/27/2006. This chart represents only male patients. 13

Fact Count Growth & Survival Chart (Male) Horizontal blue curves with rectangular labels are five year fact count percentiles. Vertical red curves with hexagonal labels are three year survival curves. Data are from BWH and MGH from 7/28/2001 to 7/27/2009. All patients had at least one fact between 7/28/2005 and 7/27/2006. The current age is the patient age on 7/27/2006. This chart represents only male patients. Fact Count Growth & Survival Chart (Male) Horizontal blue curves with rectangular labels are five year fact count percentiles. Vertical red curves with hexagonal labels are three year survival curves. Data are from BWH and MGH from 7/28/2001 to 7/27/2009. All patients had at least one fact between 7/28/2005 and 7/27/2006. The current age is the patient age on 7/27/2006. This chart represents only male patients. 14

Fact Count Growth & Survival Chart (Female) Horizontal blue curves with rectangular labels are five year fact count percentiles. Vertical red curves with hexagonal labels are three year survival curves. Data are from BWH and MGH from 7/28/2001 to 7/27/2009. All patients had at least one fact between 7/28/2005 and 7/27/2006. The current age is the patient age on 7/27/2006. This chart represents only female patients. Adding HSD To i2b2 15

Adding HSD to i2b2 Using Sci2 Visualization Platform Software and demos available at http://healthcaresystemdynamics.org Ontology for Visualizing HSD Informatics for Integrating Biology and the Bedside https://www.i2b2.org Interactive HSD Visualizations Science of Science (Sci2) https://sci2.cns.iu.edu HSD Visualization Ontology Clinical Data Repository (i2b2) Selected Patients & Concepts HSD Data Cube (XML) HSD Data Visualization (Sci2) Extend Ontology Refine Queries Learning from Visualization of HSD HSD Ontology for i2b2 16

Sci2 WBC Survival by Time of Day Heatmap Sci2 WBC Survival by Time of Day Heatmap 17

We need your help! Try our i2b2 demo & download HSD ontology http://healthcaresystemdynamics.org Participate in our upcoming Focus Groups Share with us references of similar work Feedback, suggestions, questions, etc. weber@hms.harvard.edu Healthcare System Dynamics http://healthcaresystemdynamics.org Denis Agniel 1, Nick Benik 1, Katy Borner 2, Nick Brown 1, Daniel Halsey 2, Isaac Kohane 1, Daniel O Donnell 2, Griffin Weber 1 1 Harvard Medical School; 2 Indiana University weber@hms.harvard.edu Big Data to Knowledge (BD2K), NIH/NCI U01 CA198934 18

Extras Clinical Trial vs EHR Data Clinical Trial (With 6 Month Visits) Hospital Electronic Health Record 19

Survival by WBC Value and HSD Dimensions Survival by WBC Value and HSD Dimensions 20

Survival 3 Years After a WBC Test (White, Male, 50-69 Years; Using Last WBC Between 7/28/05 and 7/27/06) Survival 3 Years After a WBC Test (White, Male, 50-69 Years; Using Last WBC Between 7/28/05 and 7/27/06) 21

Age-Fact Count Disease Profiles Each point is the average age and fact count percentile of patients with a diagnosis Cosmetic surgery vs. vascular catheter: same patient age, different health statuses 22